code: def bounded_regress_loss(
      landmark_gt, landmarks_t, landmarks_s, reg_m=0.5, br_alpha=0.05
  ):
      """
      Calculate the Bounded Regression Loss for Knowledge Distillation.
  
      Parameters
      ----------
      landmark_gt : tensor
          the ground truth of landmarks
      landmarks_t : tensor
          the predicted landmarks of teacher
      landmarks_s : tensor
          the predicted landmarks of student
      reg_m : float32
          the value to control the regresion constraint
      br_alpha : float32
          the balance value for kd loss
  
      Returns
      -------
      output: tensor
          the bounded regression loss
      """
      l2_dis_s = (landmark_gt - landmarks_s).pow(2).sum(1)
      l2_dis_s_m = l2_dis_s + reg_m
  
      l2_dis_t = (landmark_gt - landmarks_t).pow(2).sum(1)
      br_loss = l2_dis_s[l2_dis_s_m > l2_dis_t].sum()
  
      return br_loss * br_alpha
  
file_path: nni/examples/nas/legacy/oneshot/pfld/lib/utils.py
imports: from __future__ import absolute_import, division, print_function
  import torch
  import numpy
  import torch.nn
label_desc: Use image node_id 2 for calling a global function with example usage: bounded_regress_loss(landmark_gt, landmarks_t, landmarks_s, reg_m, br_alpha) and returns: unknown